Coverage Policy Manual
Policy #: 2016014
Category: Laboratory
Initiated: June 2016
Last Review: June 2022
  Genetic Test: Use of Common Genetic Variants (Single Nucleotide Polymorphisms) to Predict Risk of Nonfamilial Breast Cancer

Description:
THIS POLICY REPLACES ARCHIVED POLICIES #2009030 AND #2010026
 
Rare, single gene variants conferring a high risk of breast cancer have been linked to hereditary breast cancer syndromes. Examples are mutations in BRCA1 and BRCA2. These, and a few others, account for less than 25% of inherited breast cancer. Moderate risk alleles, such as variants in the CHEK2 gene, are also relatively rare and apparently explain very little of the genetic risk.  
 
In contrast, several common single nucleotide variants (SNVs) associated with breast cancer have been identified primarily through genome-wide association studies (GWAS) of very large case-control populations. These alleles occur with high frequency in the general population, although the increased breast cancer risk associated with each is very small relative to the general population risk. Some have suggested that these common-risk SNPs could be combined for individualized risk prediction either alone or in combination with traditional predictors; personalized breast cancer screening programs could then vary by starting age and intensity according to risk. Along these lines, the American Cancer Society recommends that women at high risk (>20% lifetime risk) should undergo breast magnetic resonance imaging (MRI) and a mammogram every year, and those at moderately increased risk (15%-20% lifetime risk) should talk with their doctors about the benefits and limitations of adding MRI screening to their yearly mammogram (American Cancer Society, 2015).
 
GeneType for Breast Cancer (and the previous versions of the test, BREVAGenplus® and BREVAGen®) evaluates breast cancer-associated single nucleotide variants (SNVs) identified in genome-wide association studies. The first-generation test, BREVAGen, included 7 SNVs. Currently, GeneType includes over 70 SNVs (Elicity, 2021). Risk is calculated by combining individual SNV risks with other risk factors. GeneType has been evaluated for use in African American, Caucasian, and Hispanic patient samples, age 35 years and older, who do not have a history of in situ or invasive breast cancer and are not carriers of a known pathogenic variant or rearrangement in a breast cancer susceptibility gene (Genetic Technologies, 2021).
 
Regulatory Status
Clinical laboratories may develop and validate tests in-house and market them as a laboratory service; laboratory-developed tests must meet the general regulatory standards of the Clinical Laboratory Improvement Amendments (CLIA). GeneType for Breast Cancer (Genetic Technologies) is available under the auspices of the CLIA. Laboratories that offer laboratory-developed tests must be licensed by the CLIA for high-complexity testing. To date, the U.S. Food and Drug Administration has chosen not to require any regulatory review of this test.
 
Coding
 
SNP Panel Tests
There is no specific CPT code for this test. Effective in 2013, the unlisted multianalyte assay with algorithmic analysis code 81599 would be the most appropriate code to report for this testing when results are reported as a risk score or probability.
 
Clinical Genetic Tests
There is no specific code for the GeneType for Breast Cancer test. The unlisted multianalyte assay with algorithmic analysis code 81599, which became effective in 2013, would probably be reported for these tests.

Policy/
Coverage:
Effective June 2022
 
In general, genetic cancer susceptibility panels are not covered, however, when coverage criteria of other policies are met (see policies 1998051, 2004038, 2014013, 2015004, 2015002, 2013010), limited genetic cancer susceptibility panels, including only the gene variants for which a given member qualifies, meets primary coverage criteria that there be scientific evidence of effectiveness in improving health outcomes.
 
Does Not Meet Primary Coverage Criteria Or Is Investigational For Contracts Without Primary Coverage Criteria
 
Testing for one or more single nucleotide variants (SNVs) to predict an individual’s risk of breast cancer does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness.
 
For members with contracts without primary coverage criteria, testing for one or more single nucleotide variants (SNVs) to predict an individual’s risk of breast cancer is considered investigational. Investigational services are specific contract exclusions in most member benefit certificates of coverage.
 
The GeneType® breast cancer risk test (previous versions BREVAGenplus® and BREVAGen), for all indications, including but not limited to use as a method of estimating individual patient risk for developing breast cancer, does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness.
 
For members with contracts without primary coverage criteria, the GeneType® breast cancer risk test (previous versions BREVAGenplus® and BREVAGen), for all indications, including but not limited to use as a method of estimating individual patient risk for developing breast cancer, is considered investigational. Investigational services are specific contract exclusions in most member benefit certificates of coverage.
 
Effective Prior to June 2022
 
In general, genetic cancer susceptibility panels are not covered, however, when coverage criteria of other policies are met (see policies 1998051, 2004038, 2014013, 2015004, 2015002, 2013010), limited genetic cancer susceptibility panels, including only the gene variants for which a given member qualifies, meets primary coverage criteria that there be scientific evidence of effectiveness in improving health outcomes.
 
Does Not Meet Primary Coverage Criteria Or Is Investigational For Contracts Without Primary Coverage Criteria
 
Testing for one or more single nucleotide polymorphisms (SNPs) to predict an individual’s risk of breast cancer does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness in improving health outcomes.
 
For contracts without primary coverage criteria, testing for one or more single nucleotide polymorphisms (SNPs) to predict an individual’s risk of breast cancer is considered investigational.  Investigational services are exclusions in the member benefit certificate of coverage.
 
The OncoVue® and BREVAGenplus® breast cancer risk tests, for all indications, including but not limited to use as a method of estimating individual patient risk for developing breast cancer, do not meet member benefit certificate Primary Coverage Criteria that there be scientific evidence of effectiveness in improving health outcomes.
 
For contracts without Primary Coverage Criteria, the OncoVue® and BREVAGenplus®  breast cancer risk tests, for all indications, including but not limited to use as a method of estimating individual patient risk for developing breast cancer, are considered investigational.  Investigational services are specific contract exclusions in most member benefit certificates of coverage.
 
Effective Prior to November 2020
 
 Does Not Meet Primary Coverage Criteria Or Is Investigational For Contracts Without Primary Coverage Criteria
 
Testing for one or more single nucleotide polymorphisms (SNPs) to predict an individual’s risk of breast cancer does not meet member benefit certificate primary coverage criteria that there be scientific evidence of effectiveness in improving health outcomes.
 
For contracts without primary coverage criteria, testing for one or more single nucleotide polymorphisms (SNPs) to predict an individual’s risk of breast cancer is considered investigational.  Investigational services are exclusions in the member benefit certificate of coverage.
 
The OncoVue® and BREVAGenplus® breast cancer risk tests, for all indications, including but not limited to use as a method of estimating individual patient risk for developing breast cancer, do not meet member benefit certificate Primary Coverage Criteria that there be scientific evidence of effectiveness in improving health outcomes.
 
For contracts without Primary Coverage Criteria, the OncoVue® and BREVAGenplus®  breast cancer risk tests, for all indications, including but not limited to use as a method of estimating individual patient risk for developing breast cancer, are considered investigational.  Investigational services are specific contract exclusions in most member benefit certificates of coverage.

Rationale:
This policy is being developed in June 2016 by combining the information from two separate policies #2010028, Genetic Test: Breast Ca Predict; Risk of Non-familial Cancer and #2009030 Genetic Test: Non-BRCA Breast Cancer Risk Assessment.
 
Genome-wide association studies (GWAS) examine the entire genome of thousands of subjects for single nucleotide polymorphisms (SNPs), single base-pair variations in the DNA sequence at semiregular intervals, and attempt to associate variant SNP alleles with particular diseases. Several case-control GWAS, primarily in white women, have investigated common risk markers of breast cancer. In recent years, several SNPs associated with breast cancer have been reported at a high level of statistical significance and have been validated in 2 or more large, independent studies (Stacey, 2007; Easton, 2007; Hunter, 20007; Thomas, 2009, Stacey, 2008; Gold, 2008; Ahmed, 2009; Zheng, 2009; Garcia-Closas, 2008). Recently, SNPs associated with breast cancer risk in Asian and African women have been the subject of more than a dozen articles, although these appear exploratory (Beeghly-Fadiel, 2011; Cai, 2011; Han, 2011; Jiang, 2011; Mong, 2011; Mukherjee, 2011; Ota, 2010; Ren, 2011; Yu, 2011; Ma, 2012; Dai, 2012; Long, 2012; Huo, 2012; McCarthy, 2013). GWAS also have identified SNPs in specific genes associated with the onset or severity of chemotherapy-induced toxicity (Baldwin, 2012; Romero, 2012).
 
SNP Panel Tests
As noted in the Background section, estimates of breast cancer risk, based on SNPs derived from large GWAS and/or from SNPs in other genes known to be associated with breast cancer, are available as laboratory-developed test services from different companies. The literature on these associations is growing, although information about the risk models is proprietary. Independent determination of clinical validity in an intended-use population to demonstrate clinical validity has not been performed. There are also no studies to suggest that use of SNP-based risk assessment has any impact on health care outcomes. No peer-reviewed reports have been published in which commercially available breast cancer risk estimators have been compared with each other to determine if they report similar results on the
same individuals, specifically for breast cancer.
 
Meta-Analyses
Several meta-analyses have investigated the association between breast cancer and various SNPs. Meta-analyses of case control studies have indicated that specific SNPs are associated with increased or decreased breast cancer risk (Gu, 2013; Gong, 2013; Milne, 2013; Bodelon, 2013; He, 2012; Dai, 2014; Tang, 2012; Tian, 2013; Pineda, 2013; kConFab Investigators, 2014’ Yu, 2013; Zhang, 2013; Zheng, 2014; Gao, 2014; Want, 2013; Zhong, 2013; Saadat, 2012; Qin, 2013; Chen, 2013; Perna, 2013; Zhang, 2014; Li, 2014; He, 2014; He, 2012). Other meta-analyses have revealed the interaction between environment (eg, obesity, age at menarche) (Schoeps, 2014; Nickels, 2013) or ethnicity (Pei, 2013; Wu, 2013; Liu, 2013; Zheng, 2013; Yao, 2013; Zhou, 2013) and breast cancer risk conferred by certain SNPs. Zhou et al (2013) found that a specific polymorphism in the vitamin D receptor gene increased breast cancer risk in African-American but not white women (Zhou, 2013). Breast cancer risk associated with SNPs in microRNAs is commonly modified by ethnicity (Chen, 2014; Xu, 2013; Zhong, 2013; Fan, 2013). Meta-analyses of GWAS have identified SNPs at new breast cancer susceptibility loci (Michailidou, 2013; Siddiq, 2012; Garcia-Closas, 2013). All of these markers are considered to be in an investigational phase of development.
 
In 2014, the Breast Cancer Association Consortium published a mega-analysis of 46,450 case patients and 42,461 controls from 38 international meta-analytic studies (Milne, 2014). The authors assessed 2-way interactions among 3277 breast cancer-associated SNPs. Of 2.5 billion possible 2-SNP combinations, none were statistically significantly associated with breast cancer risk. The study suggests that risk models may be simplified by eliminating interaction terms. Nonetheless, the authors cautioned that despite the large sample size, the study may have been underpowered to detect very small interaction effects, which tend to be smaller than main effects.
 
In 2014, the Breast and Prostate Cancer Cohort Consortium published a meta-analysis of 8 prospective cohort studies conducted in the United States, Europe, and Australia to examine 2-way interactions between genetic and established clinical risk factors (Joshi, 2014). Based on published GWAS, 23 SNPs were selected for analysis in 10,146 cases of invasive breast cancer and 12,760 controls. Patients were of European ancestry and matched on age and other factors specific to each study. After correction for multiple comparisons, a statistically significant excess in relative risk was attributed to interaction between rs10483813 mutations in RAD51L1 and body mass index (BMI).
 
Primary Studies
Because there are no published studies of commercial SNP-based breast cancer risk predictors, published studies of the clinical usefulness of other similar SNP combinations as risk predictors are considered here.
 
In 2015, McCarthy et al at the University of Pennsylvania examined the impact of BMI, Gail model risk, and a 12-SNP version of the deCODE BreastCancer™ test on breast cancer risk prediction and biopsy decisions among women with Breast Imaging-Reporting and Data System (BI-RADS) 4 mammograms who had been referred for biopsy (N=464) (McCarthy, 2015).The original deCODE BreastCancer™ panel included 7 SNPs; neither panel is currently commercially available. Mean patient age was 49 years, 60% were white, and 31% were black. In multivariate regression models that included age, BMI, Gail risk factors, and SNP panel risk as a continuous variable, a statistically significant association between SNP panel risk and breast cancer diagnosis was observed (odds ratio, 2.30; 95% confidence interval), 1.06 to 4.99; Hosmer-Lemeshow goodness-of-fit test, p=0.035). However, categorized SNP panel risks (eg, relative increase or decrease in risk compared with the general population), resembling how the test would be used in clinical practice, were not statistically associated with breast cancer diagnosis. In subgroups defined by black or white race, SNP panel risk also was not statistically associated with breast cancer diagnosis. Risk estimated by a model that included age, Gail risk factors, BMI, and the SNP panel, reclassified 9 women (3.4%) below a 2% risk threshold for biopsy, none of whom were diagnosed with cancer.
 
Aston et al (2005) evaluated more than 14,000 oligogenotypes, defined by 2 or more SNPs in 10 breast cancer-associated genes (Aston, 2005). The association with breast cancer was considered statistically significant for 37 oligogenotypes. The authors observed that oligogenic combinations of 2 to 10 SNPs were strongly associated with wide variation in breast cancer risk; that for many combinations, genes affected breast cancer risk in a manner not predictable from single gene effects; and that compared with individual SNPs, these combinations stratified risk over a broader range.
 
In 2008, Pharoah et al considered a combination of 7 well-validated SNPs associated with breast cancer, 5 of which are included in the deCODE BreastCancer™ test (Pharoah, 2008). A model that simply multiplies the individual risks of the 7 common SNPs was assumed, and would explain approximately 5% of the total genetic risk of nonfamilial breast cancer. Applying the model to the population of women in the U.K., the profile provided by the 7 SNPs did not provide sufficient discrimination between those who would and would not experience future breast cancer to enable individualized preventive treatment, such as tamoxifen. However, the authors suggested that a population screening program could be personalized with results of SNP panel testing. They concluded that no women would be included in the high-risk category (defined as 20% risk within the next 10 years at age 40 to 49 years, according to the National Institute for Health and Care Excellence), and therefore none would warrant the addition of magnetic resonance imaging (MRI) screening or consideration of more aggressive intervention.
 
Reeves et al (2010) evaluated the performance of a panel of 7 SNPs associated with breast cancer in 10,306 women with breast cancer and 10,383 without cancer in the U.K. (Reeves, 2010). The risk panel also contained 5 SNPs included in the deCODE BreastCancer™ test and used a similar multiplicative approach. Sensitivity studies were performed using only 4 SNPs and using 10 SNPs, both demonstrating no significant change in performance. Although the risk score showed marked differences in risk between the upper quintile of patients (8.8% cumulative risk to age 70 years) and the lower quintile of patients (4.4%), these changes were not viewed as clinically useful when compared with patients with an estimated overall background risk of 6.3%. Of note, simple information on patient histories; eg, presence of 1 or 2 first-degree relatives with breast cancer, provided equivalent or superior risk discrimination (9.1% and 15.4%, respectively). It is assumed that many more genetic risk markers remain to be discovered because substantial unexplained heritability remains (Sakoda, 2013). Researchers from the Collaborative Oncological Gene-Environment Study group, a mega-consortium established to follow-up previous GWAS and candidate gene association studies, estimate that “more than 1000 additional loci are involved in breast cancer susceptibility.”45 One reason more genetic associations have not been found is that even large GWAS are underpowered to detect uncommon genetic variants (Hunter, 2008).
 
Two approaches have recently been described to help address this problem. Braun and Buetow (2011) described a technique for multi-SNP analysis of GWAS data based on the study of patient cases selected using their association with known pathways related to disease risk (Braun, 2011). The authors coined the term Pathways of Distinction Analysis to describe this methodology and demonstrated that using this approach facilitated the identification of disease-related SNPs by creating clusters of similar variants within disease groups that stood out when compared with control groups.
 
In 2012, Silva et al reported on the use of DNA pooling methods to aid in detection of genetic Polymorphisms (Silva, 2012). They combined DNA from many individuals (up to 200 patients or controls) into a single sample in an effort to preselect SNPs of interest in different populations. They concluded that test accuracy was sufficiently robust to allow use of pooling to estimate allelic distributions in populations of interest.
 
Although there are no guidelines regarding the clinical use of SNP panels for estimating breast cancer risk, the published literature is in general agreement that their use in clinical or screening settings is premature due to a lack of a more complete set of explanatory gene variants and to insufficient discriminatory power at this time (Pharoah, 2008; Reeves, 2010; Hunter, 2008; Wacholder, 2010; Devilee, 2010; Offit, 2009; Mealiffe, 2010). Whether additional SNP studies are likely to be informative is in debate, as the study size to detect more and more rare variants becomes prohibitively large. As the cost of whole genome sequencing continues to decrease, some predict that this will become the preferred avenue for researching risk variants. Challenges to sorting through the growing literature on this diagnostic approach include nonstandardization and nontransparency of studies (Janssens, 2011). Janssens et al (2011) published a methods paper providing a road map for optimal reporting and an accompanying detailed article describing good reporting practices (Janssens, 2011).
 
In 2011, Bloss et al reported on the psychological, behavioral, and clinical effects of risk scanning in 3639 patients followed for a short time (mean [SD], 5.6 [2.4] months) (Bloss, 2011). These investigators evaluated anxiety, intake of dietary fat, and exercise based on information from genomic testing. There were no significant changes before and after testing and no increase in the number of screening tests obtained in enrolled patients. Although more than half of patients participating in the study indicated an intent to undergo screening in the future, during the course of the study itself, no actual increase was observed.
 
Section Summary
Common SNPs have been shown in primary studies and meta-analyses to be significantly associated with breast cancer risk; some SNPs convey slightly elevated risk compared with the general population risk. Panels of SNPs are commercially available, with results synthesized into breast cancer risk estimates. These have not been clinically validated and clinical utility has not been demonstrated. Non-U.S. tests are commercially available as direct-to-consumer tests. Use of such risk panels for individual patient care or for population screening programs is premature because (1) performance of these panels in the intended-use populations is uncertain, and (2) most genetic breast cancer risk has yet to be explained by undiscovered gene variants and SNPs. Long-term prospective studies with large sample
sizes are needed to determine the clinical validity and utility of SNP-based models for use in predicting breast cancer risk. The discrimination offered by the limited genetic factors currently known is insufficient to inform clinical practice.
 
Clinical Genetic Tests
 
OncoVue®
The OncoVue® test was developed by evaluating samples from a large case-control study for 117 common, functional polymorphisms, mostly SNPs, in candidate genes likely to influence breast carcinogenesis. A model using weighted combinations of 22 SNPs in 19 genes together with several Gail model (personal and family history characteristics) risk factors was subsequently identified by multiple linear regression analysis. OncoVue improved individual sample risk estimation, compared with the Gail model alone (p<0.001), by correctly placing more cases and fewer controls at elevated risk (Jupe, 2007). In the same study, the model was validated on an independent sample set with similarly significant results. To date, this study has only been published in a meeting abstract; no details of the study or its results are available. Note that the Gail model has been shown to accurately estimate the proportion of women (without a strong family history) who will develop cancer in large groups but is a poor discriminator of risk among individuals (Cummings, 2009).
 
Using the same case control validation data, OncoVue was also compared with risk estimation determined by 7 SNPs reported in other GWAS (Jupe, 2009); the GWAS risk scores were unable to stratify subjects by risk for breast cancer, whereas OncoVue significantly stratified patients by risk. This study has not been published. Independently, SNPs derived from GWAS are known to result in only low-level estimates of risk at best; in 1 example, a 14-SNP polygenic risk score yielded an odds ratio of only 1.3 for estrogen receptor (ER)‒positive breast cancer and 1.05 for ER-negative breast cancer (Reeves, 2010).
 
An additional analysis of the same case-control data was reported at the 2010 San Antonio Breast Cancer Symposium (Jupe, 2010). The OncoVue risk score was calculated in the same discovery set (4768 white women, 1592 cases, 3176 controls) and 2 independent validation sets (1137 white women, 376 cases, 761 controls; 494 African American women, 149 cases, 345 controls). For both OncoVue and Gail model risk scores, positive likelihood ratios (proportion of patients with breast cancer with an elevated risk estimate [20%] divided by the proportion of disease-free subjects with an elevated risk estimate) were calculated. OncoVue exhibited a 1.6- to 1.8-fold improvement compared with the Gail model in more accurately assigning elevated risk estimates to breast cancer cases rather than controls. At higher risk thresholds, the fold improvement increased and exceeded 2.5 in some sample sets.
 
Does OncoVue Testing Improve the Accuracy of Breast Cancer Risk Prediction Beyond Standard
Risk Prediction Measures?
The performance of OncoVue was studied in women from the Marin County, CA, Breast Cancer Adolescent Risk Factor study. A retrospective case control study was developed within the cohort, and samples were evaluated with OncoVue testing. OncoVue assigned high-risk status (defined as 12% lifetime risk of developing breast cancer) to 19 more women who had had breast cancer (of 169 cases) than did the Gail model, which represented an approximately 50% improvement (Dalessandri, 2008). OncoVue was also more effective at stratifying risk in the high-risk Marin County population than 7 SNPs reported in other GWAS (Dalessanderi, 2009). These studies have not yet been published in peer-reviewed journals.
 
Several supportive studies have been published as meeting abstracts. These address conceptual aspects of the OncoVue test but do not appear to report data using the final OncoVue test configuration. One fully published study characterizes SNPs that exhibit breast cancer risk associations that vary with age (Ralph, 2007). This study stratified breast cancer cases and normal controls into 3 age groups, then determined breast cancer risk for SNP homozygotes and heterozygotes for each of 18 candidate SNPs within each age group. Of these, 5 SNP variants had statistically significant odds ratios for at least 1 age group. In a separate validation sample, only 1 had a statistically significant odds ratio but not in a pattern similar to that of the discovery set. The other 4 SNPs, although not significant, were judged to have patterns of results similar to that of the discovery set and were investigated further by a sliding 10-year window strategy; the authors suggested that results of this analysis clarified age-specific breast cancer risk
associations. The authors noted the need for additional validation in other populations and non-white ethnicities.
 
Do Results of OncoVue Testing Lead to Changes in Management That Result in Health Outcome Improvements?
 
The medical management implications of this test are unclear. The Gail model was originally designed for use in clinical trials, not for individual patient care and management (Evans, 2007). Thus, using the Gail model as a baseline for comparison may not be sufficiently informative. Additionally, no evidence of improved outcomes as a result of management changes in OncoVue-identified high-risk patients has been presented or published. The OncoVue sample report makes no recommendations regarding patient management. The Progressive Medical Enterprises website makes this statement regarding test results:
 
“Women with Moderate or High breast cancer risk level scores should work closely with their healthcare provider to establish appropriate screening regimens, as well as to consider more sophisticated screening techniques, the use of cancer prevention medications, and implementing specific lifestyle changes to proactively manage their breast cancer risk (Progressive Medical Enterprises, 2012).”
 
A pilot study using buccal samples from women in the Marin County, CA retrospective case control study previously described aimed to examine the genotypes of subjects determined to be high risk (12%) by OncoVue® (Dalessandri, 2012). Of 22 SNPs assessed by the OncoVue assay, one (rs7975232 in the vitamin D receptor gene) occurred significantly more often in high-risk cases than in the overall (all cases plus controls) sample (64% vs 34%; p<0.001); however, the incidence among all cases (29%) was less than that among controls (39%). The authors postulated a potential prevention strategy using vitamin D supplementation in women with this genotype. Although recent retrospective studies support an association between sunlight exposure, elevated serum levels of vitamin D (25[OH]D)/vitamin D supplementation, and reduced risk of breast cancer, prospective uncontrolled studies gave mixed results (positive or no association) (van der Rhee, 2013; Bolland, 2011). Clinical trials demonstrating improved health outcomes in patients identified as high risk due to OncoVue detection of the rs7975232 SNP who were subsequently treated with vitamin D supplementation have not been reported.
 
BREVAGen and BREVAGenplus®
In 2010, Mealiffe et al published a clinical validation study of the BREVAGen® test (Mealiffe, 2010). The authors evaluated a 7-SNP panel in a nested case control cohort of 1664 case patients and 1636 controls. A model that multiplied the individual risks of the 7 SNPs was assumed, and the resulting genetic risk score was assessed as a potential replacement for or add-on test to the Gail clinical risk model. The net reclassification improvement was used to evaluate performance. Combining 7 validated SNPs with the Gail model resulted in a modest improvement in classification of breast cancer risks, but area under the curve (AUC) only increased from 0.557 to 0.594 (0.50 represents no discrimination, 1.0 perfect discrimination). The impact of reclassification on net health outcome was not evaluated. The authors suggested that best use of the test might be in patients who would benefit from enhanced or improved risk assessment, eg those classified as intermediate risk by the Gail model.
 
Information about analytic validity of the BREVAGenplus® test is provided in the published study, but is indeterminate. Genomic DNA samples were analyzed on custom oligonucleotide arrays (Affymetrix, Santa Clara, CA). Mean concordance across duplicate samples included for quality control was 99.8%; breast cancer loci had call rates (a measure of SNP detection) above 99%. For approximately 70% of samples with sufficient DNA available, whole genome amplification also was carried out using the Sequenom (San Diego, CA) MassARRAY platform. Across samples that had not been excluded for lack of DNA or poor quality data (proportion not reported), concordance between the 2 assays was 97%, and the resulting call rate was 96.8%. Genotype data for 121 samples that had 1 or more inconsistencies between the Sequenom analysis, and the corresponding custom array genotype were excluded.
Conflicting calls were not differentially distributed across case patients and controls. The authors acknowledged that the 2 assays performed “relatively poorly,” but asserted that consensus calls were nonetheless accurate.
 
In 2013, Dite et al published a similar case control study of the same 7 SNPs assuming the same multiplicative model (based on independent risks of each SNP) (Dite, 2013). Predictive ability of the Gail model with and without the 7 SNP panel was compared in 962 case patients and 463 controls, all 35 years of age or older (mean age, »45 years). AUC of the Gail model was 0.58 (95% CI, 0.54 to 0.61); in combination with the 7-SNP panel, AUC increased to 0.61 (95% CI, 0.58 to 0.64; bootstrap resampling, p<0.001). In reclassification analysis, 12% of cases and controls were correctly reclassified and 9% of cases and controls were incorrectly reclassified when the 7-SNP panel was added to the Gail model. Risk classes were defined by 5-year risk of developing breast cancer (<1.5%, 1.5% to <2.0%, and 2.0%). Although addition of the 7-SNP panel to the Gail model improved predictive accuracy, the magnitude of improvement is small, overall accuracy is moderate, and impact on health outcomes is uncertain.
 
Other Clinical Genetic Tests
Other large studies have evaluated 8 to 18 common, candidate SNPs in breast cancer cases and normal controls to determine whether breast cancer assessments based on clinical factors plus various SNP combinations were more accurate than risk assessments based on clinical factors alone.
    • Zheng et al (Zheng, 2010) found that 8 SNPs, combined with other clinical predictors, were significantly associated with breast cancer risk; the full model gave an AUC of 0.63.
    • Campa et al (Campa, 2011) evaluated 17 SNP breast cancer susceptibility loci for any interaction with established risk factors for breast cancer but found no evidence that the SNPs modified the associations between established risk factors and breast cancer.
    • Wacholder et al (Wacholder, 2010) evaluated the performance of a panel of 10 SNPs associated with breast cancer that had, at the time of the study, been validated in at least 3 published GWAS. Cases (n=5590) and controls (n=5998) from the National Cancer Institute’s Cancer Genetic Markers of Susceptibility GWAS of breast cancer were included in the study (women of primarily European ancestry). The SNP panel was examined as a risk predictor alone and in addition to readily available components of the Gail model (eg, diagnosis of atypical hyperplasia was not included). Mammographic density also was not included. The authors found that adding the SNP panel to the Gail model resulted in slightly better stratification of a woman’s risk than either the SNP panel or the Gail model alone but that this stratification was not adequate to inform clinical practice. For example, only 34% of the women who actually had breast cancer were assigned to the top 20% risk group. AUC for the combined SNP and Gail model was 62% (50% is random, 100% is perfect).
    • Darabi et al (Darabi, 2012) investigated the performance of 18 breast cancer risk SNPs, together with mammographic percentage density (PD), BMI, and clinical risk factors in predicting absolute risk of breast cancer, empirically, in a well-characterized case-control study of postmenopausal Swedish women. Performance of a risk prediction model based on an initial set of 7 breast cancer risk SNPs was improved by including 11 more recently established breast cancer risk SNPs (p<0.001). Adding mammographic PD, BMI and all 18 SNPs to a modified Gail model improved the discriminatory accuracy (the AUC statistic) from 55% to 62%. The net reclassification improvement was used to assess improvement in classification of women into 5-year low-, intermediate-, and high-risk categories (p<0.001). It was estimated that using an individualized screening strategy based on risk models incorporating clinical risk factors, mammographic density, and SNPs, would capture 10% more cases. Impacts on net health outcomes from such a change are unknown.
    • Armstrong et al (Armstrong, 2013) examined the impact of pretest breast cancer risk prediction on the classifıcation of women with an abnormal mammogram above or below the risk threshold for biopsy. Currently, 1-year probability of breast cancer among women with BI-RADS category 3 mammograms is 2%; these women undergo 6-month follow-up rather than biopsy. In contrast, women with BI-RADS4 mammograms have a 6% (BI-RADS 4A) or greater (BI-RADS 4B and 4C) probability of developing breast cancer in 1 year; these women are referred for biopsy. Using the Gail model plus 12 SNPs for risk prediction and a 2% biopsy risk threshold, 8% of women with a BI-RADS 3 mammogram were reclassified above the threshold for biopsy and 7% of women with BI-RADS 4A mammograms were reclassified below the threshold. The greatest impact on reclassification was attributed to standard breast cancer risk factors. Net health outcomes were not compared between women who were reclassified and those who were not.
 
Although results of these studies support the concept of clinical genetic tests, they do not represent direct evidence of their clinical validity or utility.
 
Section Summary
There is a lack of published detail regarding OncoVue® and BREVAGenplus® test validation, supportive data, and management implications. Available data suggest that OncoVue® and BREVAGenplus® may add predictive accuracy to the Gail model. However, the degree of improved risk prediction may be modest, and clinical implications are unclear. There is insufficient evidence to determine whether using breast cancer risk estimates from OncoVue® or BREVAGenplus® in asymptomatic individuals changes management decisions and improves patient outcomes.
 
Ongoing Clinical Trials
 
Some currently unpublished trials that might influence this policy are:
 
Ongoing
 
    • NCT00329017 Correlation of SNP Patterns With Fine Needle Aspiration Cytomorphology in High Risk Postmenopausal Women. This study has a planned enrollment of 500 subjects with an estimated completion date of May 2016.
    • NCT00122239 A Study of Gene Polymorphisms and Normal Tissue Radiation Injury in Patients Treated for Breast, Prostate, Brain, Lung, and Head and Neck Cancers. This study has a planned completion date of February 2017.
 
Summary of Evidence
Clinical utility of single nucleotide polymorphisms (SNP) panel tests and clinical genetic tests (eg, OncoVue®, BREVAGenplus®) is unknown. Information about analytic performance (reproducibility) of marketed tests is lacking. Most tests are in an investigational phase of development, having demonstrated associations between the SNPs tested and breast cancer risk. Clinical genetic tests may improve predictive accuracy of currently used clinical risk predictors. However, the magnitude of improvement is small, and clinical significance is uncertain. Whether potential harms of these tests due to false negative and false positive results are outweighed by potential benefit associated with improved risk
assessment is unknown. Use of these tests is therefore considered investigational.
 
Practice Guidelines and Position Statements
Current guidelines from the National Comprehensive Cancer Network identify the following limitations of multigene cancer panels: unknown significance of some variants, uncertain level of risk associated with most variants, and unclear guidance on risk management for carriers of some variants (NCCN, 2014).  For breast cancer risk assessment, the American Society of Clinical Oncology recommends the Gail model (nccn, 2015) or risk models for women with elevated risk based on family history (eg, Claus et al (Claus, 1994) or Tyrer-Cuzick et al (Tyrer, 2004) (NCCN, 2014; Visvanathan, 2103).
 
2017 Update
A literature search conducted using the MEDLINE database through May 2016 did not reveal any new information that would prompt a change in the coverage statement.
 
2018 Update
Annual policy review completed with a literature search using the MEDLINE database through May 2018. No new literature was identified that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
In 2015, Mavaddat et al reported a multicenter study that assessed risk stratification using 77 breast cancer-associated SNVs in 33,673 breast cancer cases and 33,381 control women of European descent (Mavaddat, 2015). Polygenic risk scores were developed based on an additive model plus pairwise interactions between SNVs. Women in the highest 1% of the polygenic risk score had a 3-fold increased risk of developing breast cancer compared with women in the middle quintile (odds ratio, 3.36; 95% CI, 2.95 to 3.83). Lifetime risk of breast cancer was16.6% for women in the highest quintile of the risk score compared with 5.2% for women in the lowest quintile. The discriminative accuracy was 0.622 (95% CI, 0.619 to 0.627).
 
 
One potential use of SNV testing is to evaluate the risk of breast cancer for chemoprevention. In 2017, Cuzick et al assessed whether a panel of 88 SNVs could improve risk prediction over traditional risk stratification using data from 2 randomized tamoxifen prevention trials (Cuzick, 2017). The study included 359 cases and 636 controls, with the 88 SNVs assessed on an Illumina OncoArray that evaluated approximately half a million SNVs. The primary outcome was breast cancer or ductal carcinoma in situ. The 88 SNV score improved discriminability above the Tyrer-Cuzick risk evaluator; however, there was modest improvement in the percentage of women who were classified as high risk. The percentage of women with a 10-year risk of recurrence of 8% or more was estimated to be 18% for Tyrer-Cuzick and 21% when the 88 SNV score was added. The SNV score did not predict which women would benefit from tamoxifen.
 
2019 Update
Annual policy review completed with a literature search using the MEDLINE database through May 2019. No new literature was identified that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
Curtit et al analyzed 8703 patients with early breast cancer who were in prospective case cohorts (SIGNAL and PHARE) (Curtit, 2017).https://www.evidencepositioningsystem.com/_w_cf651dcc797a00c459b3cf8f0f7cc4e43ef1de1db2a3bf27/ The primary aim was to identify associations between a 94-SNV risk score, drawn from previous literature, and invasive disease-free survival. Patients in different quartiles of the 94-SNV risk score were assessed for invasive disease-free survival and overall survival but showed no significant difference between groups (invasive disease-free survival hazard ratio, 0.993; 95% CI, 0.981 to 1.005; p=0.26). Prognostic factors such as age at diagnosis, size of tumor, and metastasis status did not correlate with the risk score, which further did not distinguish between the 3 breast cancer subtypes represented in this analysis (triple-negative, human epidermal growth factor receptor 2-positive, and hormone receptor-positive human epidermal growth factor receptor 2-negative).
 
2020 Update
A literature search was conducted through May 2020.  There was no new information identified that would prompt a change in the coverage statement.  
 
2021 Update
Annual policy review completed with a literature search using the MEDLINE database through May 2021. No new literature was identified that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
In its guidelines on genetic or familial high-risk assessment of breast and ovarian cancers (v.1.2020), the National Comprehensive Cancer Network notes the potential for multigene testing to identify intermediate penetrance (moderate risk) genes, but adds that “For many of these genes, there are limited data on the degree of cancer risk and there are no clear guidelines on risk management for carriers of pathogenic/likely pathogenic variants. Not all genes included on available multi-gene tests are necessarily clinically actionable” (NCCN, 2020). In the absence of evidence, guiding follow-up to testing, including risk management strategies, National Comprehensive Cancer Network recommends "that multi-gene testing is ideally offered in the context of professional genetic expertise, for pre- and post-test counseling” (NCCN, 2020).
 
2022 Update
Annual policy review completed with a literature search using the MEDLINE database through May 2022. No new literature was identified that would prompt a change in the coverage statement. The key identified literature is summarized below.
 
In the 2015 guidelines on genetic and genomic testing for cancer susceptibility, the American Society of Clinical Oncology (ASCO) acknowledges the role of multi-panel gene testing for high-penetrance genes of established clinical utility; however, "panel testing may identify mutations in genes associated with moderate or low cancer risks" and "testing will also identify variants of uncertain significance in a substantial proportion of patient cases" (Robson, 2015).

CPT/HCPCS:
81479Unlisted molecular pathology procedure
81599Unlisted multianalyte assay with algorithmic analysis
99090Analysis of clinical data stored in computers (eg, ECGs, blood pressures, hematologic data)

References: Allman R, Dite GS, Hopper JL, et al.(2015) SNPs and breast cancer risk prediction for African American and Hispanic women. Breast Cancer Res Treat. Dec 2015;154(3):583-589. PMID 26589314

Cuzick J, Brentnall AR, Segal C, et al.(2017) Impact of a panel of 88 single nucleotide polymorphisms on the risk of breast cancer in high-risk women: results from two randomized tamoxifen prevention trials. J Clin Oncol. Mar 2017;35(7):743-750. PMID 28029312

Elicity.(2021) Breast Cancer Risk Assessment Test Kit. 2021; https://elicity.health/our-tests/details/breast-cancer-risk. Accessed August 19, 2021.

Genetic Technologies.(2021) GeneType for Breast Cancer. https://genetype.com/for-patients/breast-cancer/. Accessed August 19, 2021.

Mavaddat N, Pharoah PD, Michailidou K, et al.(2015) Prediction of breast cancer risk based on profiling with common genetic variants. J Natl Cancer Inst. May 2015;107(5):djv036. PMID 25855707

National Comprehensive Cancer Network (NCCN).(2020) NCCN clinical practice guidelines in oncology: genetic/familial high-risk assessment: breast and ovarian. Version 1.2020. https://www.nccn.org/professionals/physician_gls/pdf/genetics_screening.pdf. Accessed August 31, 2020.

Robson ME, Bradbury AR, Arun B, et al.(2015) American Society of Clinical Oncology Policy Statement Update: Genetic and Genomic Testing for Cancer Susceptibility. J Clin Oncol. Nov 01 2015; 33(31): 3660-7. PMID 26324357


Group specific policy will supersede this policy when applicable. This policy does not apply to the Wal-Mart Associates Group Health Plan participants or to the Tyson Group Health Plan participants.
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